Use case

Optimizing airport staff scheduling using artificial intelligence

AI optimizes airport staff scheduling for passengers with reduced mobility (PRM) by predicting demand and allocating resources efficiently. This reduces wait times, improves flight punctuality, and provides predictable staff schedules.


Each day, airports witness a steady stream of passengers with reduced mobility (PRM) who require assistance throughout their journey within the terminals, from arrival to departure. Providing comprehensive support to these individuals demands a significant investment in manpower and resources.


By gaining a precise insight into the anticipated daily count of passengers with reduced mobility (PRM), it becomes feasible to establish suitable staffing levels to efficiently cater to their requirements without straining resources or exceeding budgetary limits. AI-driven systems can enhance the efficiency of PRM assistance workflows, while predictive analytics can optimize resource allocation and minimize wait times for PRM passengers.


  • Data Collection

The first step involved obtaining a data sample from a client. This data included information about flight schedules, staff availability, and the needs of assengers with reduced mobility. In addition to incorporating the client data, external actors like weather predictions and seasonal patterns were also considered.

  • Correlation Analysis

The next step was to analyze this data and derive correlation coefficients. These coefficients measure the statistical relationships between different variables in the data set.

  • Algorithm Analysis

After evaluating the data's characteristics and the correlations discovered, we compared several data prediction algorithms.

  • Algorithm Selection

The algorithm that yielded the best results from the data the sample was chosen and then trained using all available data to ensure it was prepared for use.


Adjusting staffing levels according to traffic fluctuations can help find the optimal number of staff available, contributing to safety and service quality.


  • By predicting demand and scheduling staff accordingly, airports can ensure that there are enough personnel during peak times to handle the influx of passengers.
  • Passengers' waiting times can be reduced significantly when the appropriate staff members are in the right positions to attend to them efficiently.
  • Efficient scheduling can also improve the punctuality of flights.
  • Staff members can enjoy more predictable schedules, benefiting from automated, dynamic planning.


  • Set up the data pipelines for real-time data collection and ensure the model is continuously updated.
  • Access immediate insights and forecasts using tailor-made dashboards.
  • Use interactive dashboards to simulate different scenarios, validate results, and adjust as needed.

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